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市場調查報告書
商品編碼
1836498

交通號誌識別：市​​場佔有率分析、產業趨勢、統計數據和成長預測（2025-2030 年）

Traffic Signal Recognition - Market Share Analysis, Industry Trends & Statistics, Growth Forecasts (2025 - 2030)
出版日期: | 出版商: Mordor Intelligence | 英文 150 Pages | 商品交期: 2-3個工作天內

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簡介目錄

交通號誌辨識市場預計在 2025 年價值 62.7 億美元，預計到 2030 年將達到 76.8 億美元，預測期內（2025-2030 年）的複合年成長率為 4.14%。

交通號誌識別-市場-IMG1

監管規定、攝影機價格下降以及 Level 2 Plus 自動駕駛技術正在將目標市場拓展至高階車型之外。原廠配套量如今為感測器供應商提供了必要的規模經濟效益，使其能夠將攝影機成本控制在至關重要的 10 美元閾值以下。同時，軟體的增強功能正在提升識別準確性，這將有助於保險遠端資訊處理評分和未來的自動駕駛汽車認證。

全球交通號誌識別市場趨勢與洞察

ADAS的監理整合

具有約束力的法規已將交通號誌偵測從選購附加功能轉變為必備安全功能。歐盟《通用安全法規 II》（2024 年 7 月生效）要求所有新車型必須配備由交通號誌輸入支援的智慧限速援助，而美國國家公路交通安全管理局 (NHTSA) 關於自動緊急煞車的規定則推動了對感知套件的需求。 OEM 廠商目前正在圍繞可擴展感知能力構建電氣系統，以應對未來的規則制定，從而有效地鎖定了交通號誌識別市場多年的需求。

相機成本曲線降至每台 10 美元以下

影像感測器終於克服了曾經阻礙大眾市場車輛採用先進視覺技術的成本障礙。SONY的車用CMOS藍圖和安森美半導體的3µm像素製程已將HDR提升至120 dB，並將暗電流降低了28倍，同時將單價降至10美元以下。更低的價格使OEM廠商能夠在每輛車上部署八個或更多鏡頭，從而提供更多觀點，從而提高在眩光、背光和LED閃爍場景下的識別精度。

極端天氣下能見度差、知覺力差

雪、霧和暴雨持續影響攝影機性能，導致影像品質下降，損害了公眾的信任。像 Snow-CLOC（冬季檢測準確率高達 86.61%）這樣的實驗室成功案例尚未轉化為統一的道路行駛性能，這促使開發人員轉向雷射雷達與攝影機融合和熱感疊加技術。可靠性方面的差距給監管機構帶來了沉重的負擔，他們要求在推進免持駕駛立法之前，必須明確定義性能範圍。

報告中分析的其他促進因素和限制因素

  1. 2級以上自動駕駛的普及
  2. 支援 V2I 的動態標誌更新
  3. 各國資料集檢驗成本

細分分析

到2024年，基於顏色的檢測將佔交通號誌識別市場收益的46.18%，證明了RGB閾值的長期應用。然而，到2030年，深度學習偵測的複合年成長率將達到4.71%，因為卷積網路和變壓器網路在遮蔽和光照變化條件下表現出色。預計到2030年，深度學習偵測的交通號誌辨識市場規模將呈指數級成長，這反映了OEM廠商對可透過軟體升級的精度提升的偏好。

YOLOv5 和基於注意力機制的網路目前實現了超過 95% 的準確率和超過 98% 的召回率，同時推理時間保持在 45 毫秒以內，滿足即時安全預算。隨著無線傳輸管道的成熟，汽車製造商將能夠使用邊緣影像重新訓練模型，並在無需更換硬體的情況下推送更新。交通號誌辨識市場正持續向以 AI 為中心的技術堆疊轉型，其差異化在於資料管理而非電路設計。

2024年，攝影機系統將佔交通號誌辨識市場收益的63.21%，這得益於系統效率和功能的提升，這得益於BOM成本的降低以及先進軟體工具的整合。這一主導地位凸顯了攝影機系統在交通號誌辨識應用中的日益普及。儘管成長率有所放緩，但預計到2030年，在技術持續進步的推動下，交通號誌辨識攝影機系統的市場規模將以強勁的複合年成長率飆升。

LiDAR與攝影機融合是成長最快的細分市場，複合年成長率高達 4.34%，禾賽等供應商計劃降價50%，以將固態單元納入主流車型。 SparseLIF 和類似框架將點雲幾何與影像紋理對齊，從而創造冗餘，即使在眩光或降水遮擋攝影機的情況下也能保持識別。雷達輔助選項增加了速度向量來預測相位變化，這反映了多感測器融合如何再形成交通號誌辨識市場。

區域分析

預計到2024年，亞太地區將佔據交通號誌辨識市場最大的收入佔有率，達到38.75%，到2030年，複合年成長率將達到4.21%。國內法規、智慧城市試點和垂直整合正在推動中國、日本和韓國交通號誌識別市場的集中度。深圳的V2I走廊為測試車輛提供即時相位圖，而東京的感測器產業則為全球原始設備製造商提供HDR成像儀。該地區的高密度交通創造了多樣化的資料集，增強了深度學習的穩健性，並加快了全球檢驗週期。

歐洲受通用安全法規II的驅動。大陸集團新成立的Aumovio部門和博世的感測器融合套件正在支援供應，而嚴格的網路安全規則正在塑造資料處理架構。德國和英國的保險遠端資訊處理系統因合規而獲得獎勵，這推動了消費者對交通號誌識別的需求。

北美正受益於聯邦安全法規和新創企業自動駕駛測試。在加州，嚴格的法規要求供應商記錄故障安全性能，而加拿大的冬季氣候則為惡劣天氣測試提供了天然的試驗場。這些因素共同造就了一個多元化但又相互連結的地區，一個地區的進步可以透過遍布全球的原始設備製造商計畫迅速傳播到其他地區。

其他福利：

  • Excel 格式的市場預測 (ME) 表
  • 3個月的分析師支持

目錄

第1章 引言

  • 研究假設和市場定義
  • 調查範圍

第2章調查方法

第3章執行摘要

第4章 市場狀況

  • 市場概況
  • 市場促進因素
    • ADAS 的監管吸收
    • 相機成本曲線低於 10 美元/台
    • 2級以上自動駕駛的普及
    • 使用 V2I 進行動態標誌更新
    • 高清地圖數位雙胞胎構建
    • 保險遠距資訊處理獎勵TSR準確性
  • 市場限制
    • 極端天氣條件下能見度差和能見度低
    • 特定國家/地區的資料檢驗成本
    • 虛假標籤的網路責任
    • OEM 預算轉向競爭性感測器
  • 價值/供應鏈分析
  • 監管狀況
  • 技術展望
  • 五力分析
    • 新進入者的威脅
    • 買家/消費者的議價能力
    • 供應商的議價能力
    • 替代品的威脅
    • 競爭對手之間的競爭強度

第5章市場規模與成長預測：價值（美元）

  • 依檢測方法
    • 基於顏色的檢測
    • 基於形狀的檢測
    • 特徵/深度學習檢測
  • 透過感測器技術
    • 網路攝影系統
    • 雷達輔助TSR
    • LiDAR 與攝影機融合
    • 多模態
  • 按車輛類型
    • 搭乘用車
    • 輕型商用車
    • 中大型商用車
    • 公車和長途客車
  • 按組件
    • 硬體
    • 軟體
  • 按最終用戶
    • OEM安裝
    • 售後市場
  • 按地區
    • 北美洲
      • 美國
      • 加拿大
      • 北美其他地區
    • 南美洲
      • 巴西
      • 阿根廷
      • 其他南美
    • 歐洲
      • 德國
      • 英國
      • 法國
      • 西班牙
      • 義大利
      • 其他歐洲國家
    • 亞太地區
      • 中國
      • 日本
      • 韓國
      • 印度
      • 其他亞太地區
    • 中東和非洲
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 埃及
      • 南非
      • 其他中東和非洲地區

第6章 競爭態勢

  • 市場集中度
  • 策略趨勢
  • 市佔率分析
  • 公司簡介
    • Continental AG
    • Robert Bosch GmbH
    • DENSO Corporation
    • Mobileye（Intel）
    • ZF Friedrichshafen AG
    • HELLA（FORVIA）
    • Aptiv PLC
    • Valeo SA
    • Magna International Inc.
    • Panasonic Holdings
    • Hyundai Mobis
    • Hitachi Astemo
    • Veoneer/S-SW
    • Ford Motor Company
    • General Motors Company
    • Tesla Inc.
    • NVIDIA Corporation

第7章 市場機會與未來展望

簡介目錄
Product Code: 50398

The Traffic Signal Recognition Market size is estimated at USD 6.27 billion in 2025, and is expected to reach USD 7.68 billion by 2030, at a CAGR of 4.14% during the forecast period (2025-2030).

Traffic Signal Recognition - Market - IMG1

Regulatory mandates, lower camera prices, and Level-2 Plus autonomy expand the addressable base beyond premium models. Original-equipment volumes now give sensor suppliers the economies of scale needed to hold camera costs below the pivotal USD 10 threshold. At the same time, software enhancements sharpen recognition accuracy that feeds insurance telematics scoring and future autonomous-vehicle certification.

Global Traffic Signal Recognition Market Trends and Insights

Regulation-Mandated ADAS Inclusion

Binding rules have turned traffic light detection from an optional extra into a required safety feature. The European Union's General Safety Regulation II, effective July 2024, obliges every new model to include intelligent speed assistance underpinned by traffic-signal inputs, while a parallel NHTSA rule on automatic emergency braking heightens demand for perception suites. OEMs now architect their electrical systems around scalable perception capacity that anticipates future rule-making, effectively locking in multi-year demand for the traffic signal recognition market.

Camera Cost Curve Drops Below USD 10/Unit

Imaging sensors finally cleared the cost hurdle that once kept advanced vision off mass-market vehicles. Sony's automotive CMOS roadmap and onsemi's 3 µm pixel process have driven unit prices beneath USD 10 while boosting HDR to 120 dB and cutting dark current by 28 times. Low prices let OEMs deploy eight or more cameras per vehicle, multiplying viewpoints that collectively raise recognition precision across glare, back-light, and LED-flicker scenarios.

Poor Recognition in Low-Visibility and Weather Eextremes

Snow, fog, and heavy rain still challenge cameras, creating service drops that undermine public trust. Laboratory gains such as Snow-CLOCs (86.61% detection accuracy in winter) have yet to translate into uniform street performance, pushing developers toward LiDAR-camera fusion and thermal overlays. Reliability gaps weigh on regulators who demand clearly defined performance envelopes before advancing hands-free legislation.

Other drivers and restraints analyzed in the detailed report include:

  1. Level-2 Plus Autonomy Proliferation
  2. V2I-Enabled Dynamic Sign Updates
  3. Country-Specific Dataset Validation Costs

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Color-based Detection held 46.18% of the traffic signal recognition market revenue in 2024, a testament to its long-standing use of RGB thresholds. Yet Deep-Learning Detection is rising at 4.71% CAGR to 2030 as convolutional and transformer networks prove superior under occlusion and variable lighting. The traffic signal recognition market size for Deep-Learning Detection is projected to grow exponentially by 2030, reflecting OEM preferences for software-upgradable accuracy gains.

YOLOv5 and attention-based networks now achieve more than 95% precision and over 98% recall while keeping inference below 45 ms, meeting real-time safety budgets. As over-the-air pipelines mature, automakers can retrain models on edge-case footage and push updates without hardware swaps. The traffic signal recognition market continues transitioning toward AI-centric stacks where differentiators sit in data curation, not circuit design.

In 2024, Camera Systems captured 63.21% of the traffic signal recognition market revenue, driven by declining BOM costs and the integration of advanced software tooling, which enhanced system efficiency and functionality. This dominance highlights the growing adoption of Camera Systems in traffic signal recognition applications. Despite a moderation in growth rates, the market size for Camera Systems in traffic signal recognition is projected to surge at a strong CAGR through 2030, supported by continuous technological advancements.

LiDAR-Camera Fusion is the fastest-advancing sub-segment at 4.34% CAGR as suppliers like Hesai plan 50% price cuts that place solid-state units within mainstream trims. SparseLIF and similar frameworks align point-cloud geometry with image texture, producing redundancy that sustains recognition when glare or precipitation blinds cameras. Radar-assisted options add speed vectors that predict phase changes, illustrating how multi-sensor blending reshapes the traffic signal recognition market.

The Traffic Signal Recognition Market Report is Segmented by Detection Method (Color-Based Detection, Shape-Based Detection, and Feature-Based Detection), Sensor Technology (Camera Systems, Radar-Assisted TSR, and More), Vehicle Type (Passenger Cars and More), Component (Hardware and Software), End-User (OEM and Aftermarket), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Geography Analysis

Asia-Pacific led with 38.75% of the traffic signal recognition market revenue share in 2024 and is expected to grow at a 4.21% CAGR through 2030. Domestic regulations, smart-city pilots, and vertical integration concentrate the traffic signal recognition market in China, Japan, and South Korea. Shenzhen's V2I corridors feed real-time phase maps to test fleets, while Tokyo's sensor industry supplies HDR imagers to global OEMs. Regional high-density traffic creates diverse datasets that sharpen deep-learning robustness and accelerate global validation cycles.

Europe follows, propelled by General Safety Regulation II. Continental's new Aumovio division and Bosch's sensor-fusion suites anchor supply, while stringent cybersecurity rules shape data-handling architectures. Insurance telematics in Germany and the United Kingdom reward verified compliance, channeling consumer demand into the traffic signal recognition market.

North America benefits from federal safety mandates and venture-backed autonomy pilots. California's edge-case legal scrutiny pushes suppliers to document fail-safe performance, while Canada's winter climate provides natural laboratories for adverse-weather testing. Together, these factors sustain a diversified yet interconnected geography where advances in one region ripple quickly to others through globally distributed OEM programs.

  1. Continental AG
  2. Robert Bosch GmbH
  3. DENSO Corporation
  4. Mobileye (Intel)
  5. ZF Friedrichshafen AG
  6. HELLA (FORVIA)
  7. Aptiv PLC
  8. Valeo SA
  9. Magna International Inc.
  10. Panasonic Holdings
  11. Hyundai Mobis
  12. Hitachi Astemo
  13. Veoneer / S-SW
  14. Ford Motor Company
  15. General Motors Company
  16. Tesla Inc.
  17. NVIDIA Corporation

Additional Benefits:

  • The market estimate (ME) sheet in Excel format
  • 3 months of analyst support

TABLE OF CONTENTS

1 Introduction

  • 1.1 Study Assumptions & Market Definition
  • 1.2 Scope of the Study

2 Research Methodology

3 Executive Summary

4 Market Landscape

  • 4.1 Market Overview
  • 4.2 Market Drivers
    • 4.2.1 Regulation-mandated ADAS inclusion
    • 4.2.2 Camera Cost Curve Drops Below USD10/unit
    • 4.2.3 Level-2 Plus Autonomy Proliferation
    • 4.2.4 V2I-Enabled Dynamic Sign Updates
    • 4.2.5 HD-Map Digital-Twin Build-Outs
    • 4.2.6 Insurance Telematics Rewarding TSR Accuracy
  • 4.3 Market Restraints
    • 4.3.1 Poor Recognition in Low-Visibility and Weather Extremes
    • 4.3.2 Country-Specific Dataset Validation Costs
    • 4.3.3 Cyber-Liability For Erroneous Sign Display
    • 4.3.4 OEM Budget Shift to Competing Sensor Priorities
  • 4.4 Value / Supply-Chain Analysis
  • 4.5 Regulatory Landscape
  • 4.6 Technological Outlook
  • 4.7 Porter's Five Forces
    • 4.7.1 Threat of New Entrants
    • 4.7.2 Bargaining Power of Buyers/Consumers
    • 4.7.3 Bargaining Power of Suppliers
    • 4.7.4 Threat of Substitute Products
    • 4.7.5 Intensity of Competitive Rivalry

5 Market Size & Growth Forecasts (Value (USD))

  • 5.1 By Detection Method
    • 5.1.1 Color-based Detection
    • 5.1.2 Shape-based Detection
    • 5.1.3 Feature / Deep-Learning Detection
  • 5.2 By Sensor Technology
    • 5.2.1 Camera Systems
    • 5.2.2 Radar-Assisted TSR
    • 5.2.3 LiDAR-Camera Fusion
    • 5.2.4 Multi-modal
  • 5.3 By Vehicle Type
    • 5.3.1 Passenger Cars
    • 5.3.2 Light Commercial Vehicles
    • 5.3.3 Medium and Heavy Commercial Vehicles
    • 5.3.4 Buses and Coaches
  • 5.4 By Component
    • 5.4.1 Hardware
    • 5.4.2 Software
  • 5.5 By End-User
    • 5.5.1 OEM-installed
    • 5.5.2 Aftermarket Retrofit
  • 5.6 Geography
    • 5.6.1 North America
      • 5.6.1.1 United States
      • 5.6.1.2 Canada
      • 5.6.1.3 Rest of North America
    • 5.6.2 South America
      • 5.6.2.1 Brazil
      • 5.6.2.2 Argentina
      • 5.6.2.3 Rest of South America
    • 5.6.3 Europe
      • 5.6.3.1 Germany
      • 5.6.3.2 United Kingdom
      • 5.6.3.3 France
      • 5.6.3.4 Spain
      • 5.6.3.5 Italy
      • 5.6.3.6 Rest of Europe
    • 5.6.4 Asia-Pacific
      • 5.6.4.1 China
      • 5.6.4.2 Japan
      • 5.6.4.3 South Korea
      • 5.6.4.4 India
      • 5.6.4.5 Rest of Asia-Pacific
    • 5.6.5 Middle East and Africa
      • 5.6.5.1 Saudi Arabia
      • 5.6.5.2 United Arab Emirates
      • 5.6.5.3 Egypt
      • 5.6.5.4 South Africa
      • 5.6.5.5 Rest of Middle East and Africa

6 Competitive Landscape

  • 6.1 Market Concentration
  • 6.2 Strategic Moves
  • 6.3 Market Share Analysis
  • 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as Available, Strategic Information, Market Rank/Share for Key Companies, Products and Services, SWOT Analysis, and Recent Developments)
    • 6.4.1 Continental AG
    • 6.4.2 Robert Bosch GmbH
    • 6.4.3 DENSO Corporation
    • 6.4.4 Mobileye (Intel)
    • 6.4.5 ZF Friedrichshafen AG
    • 6.4.6 HELLA (FORVIA)
    • 6.4.7 Aptiv PLC
    • 6.4.8 Valeo SA
    • 6.4.9 Magna International Inc.
    • 6.4.10 Panasonic Holdings
    • 6.4.11 Hyundai Mobis
    • 6.4.12 Hitachi Astemo
    • 6.4.13 Veoneer / S-SW
    • 6.4.14 Ford Motor Company
    • 6.4.15 General Motors Company
    • 6.4.16 Tesla Inc.
    • 6.4.17 NVIDIA Corporation

7 Market Opportunities & Future Outlook

  • 7.1 White-space & Unmet-need Assessment